Electric energy demand forecasting with neural networks

Carmona, D.; Jaramillo, M.A.; González, E.; Andreu, Joaquín

doi:10.1109/iecon.2002.1185254

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…1) from January 1975 to December 2002 (a total of 336 values) has been used to validate the proposed model. All these data has been divided into two blocks: one for training (from January 1975 to December 1997, 276 months) and the other for validation (the remaining information, 60 months) [1].…”

Section: Monthly Electric Energy Consumptionmentioning

confidence: 99%

Electric Energy Monthly Demand Forecasting by Spectral Analysis

Jaramillo

González

Carmona

et al. 2005

REPQJ

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Medium-term load forecasting is a useful tool for the maintenance planning of grids and as a market research of electric energy. In this work medium-term load forecasting methods are developed, the most forgotten time scaling process in the load forecasting bibliography. These methods will be applied to the peninsular Spanish monthly energy consumption. Methods traditionally employed with this objective are based on regression, statistical techniques (mainly Box-Jenkins ARIMA), and also with neural networks, fuzzy logic or expert systems. Most of them need the use of nonelectric variables, mainly climatic or economic ones, which strongly influence electric energy demand. These variables, of cyclic nature, provide a periodic behaviour to the energy consumption time series. This work presents a study of this periodic behaviour by means of spectral analysis, with the identification and interpretation of the dominant frequencies. A forecasting method for future values of electric energy demand will be then presented, which is based on a simple regression technique combined with neural networks. It does not take into account any climatic or economic variables, because only periodic behaviour of the time series is considered. Acceptable results are reached, with percentage errors lower than 5 % in most cases.

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Section: Monthly Electric Energy Consumptionmentioning

confidence: 99%

Electric Energy Monthly Demand Forecasting by Spectral Analysis

Jaramillo

González

Carmona

et al. 2005

REPQJ

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“…() Phimphachanh et al presented a model of load forecasting using a recurrent neural network, mainly considering existing gross domestic product (GDP) data and the number of air conditioners available. Carmona et al applied an artificial neural network (ANN) model, taking into consideration only the historic demand model. Another study, which only considers demand, also applied the method based on particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Miranda

Abaide

Sperandio

et al. 2018

Int Trans Electr Energ Syst

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Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

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“…Several layer numbers and sizes have been tested to find out the best one [5]. Some one layer and multilayer structures have been tested with one, two and three hidden units.…”

Section: Network Structurementioning

confidence: 99%

“…Nevertheless in spite of its potential interest for electric energy companies, long term forecasting has received little attention from researchers in contrast with the higher interest that short term one has had [5]. In this work a monthly demand prediction is carried out by a neural network to study the performance of this kind of forecasting.…”

Section: Introductionmentioning

confidence: 99%

Reability of the Forecasting of the Monthly Demand of Electric Energy with Neural Networks

Jaramillo¹,

Carmona²,

González³

et al. 2003

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Abstract. Electric energy demand forecasting represents a fundamental tool to plan the activities of the companies that generate and distribute it. So a good prediction of its demand will provide an invaluable information to plan the production and purchase policies of these companies. This demand may be seen as a temporal series when these data are conveniently arranged. In this way the prediction of a future value may be performed studying the past ones. Neural networks have proved to be a very powerful tool to do this. They are mathematical structures that mimic that of the nervous system of living beings and are used extensively for system identification and prediction of their future evolution. In this work a neural network is presented to predict the evolution of the monthly demand of electric consumption. A Feedforward Multilayer Perceptron (MLP) with three hidden layers has been used as neural model with Backpropagation as learning strategy. The consumption data have been normalized to avoid their rising trend. Several procedures have been tested in order to find out those performing the best. Errors smaller than 5% have been obtained in most of the predictions.

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Electric energy demand forecasting with neural networks

Cited by 12 publications

References 9 publications

Electric Energy Monthly Demand Forecasting by Spectral Analysis

Electric Energy Monthly Demand Forecasting by Spectral Analysis

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Reability of the Forecasting of the Monthly Demand of Electric Energy with Neural Networks

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